Systems incorporating several sensors are used in a great variety of sectors, such as site surveillance, maintenance, robotics, meteorological forecasts, but also the programming of systems or devices such as TV program recordings. Such systems can also be implemented in systems for interpreting information arising from the media. The merging of information or data is a paramount process in decision making, and is so whatever the sector of interest in which the decision is made. Indeed, the first step in a decision making process is the collection of information or data making it possible to evaluate a situation. This information can originate from varied sources and be expressed in various formats or media. Once collected, the information must be combined and arranged so as to obtain an overall but summary view of the situation. This combining of information of heterogeneous nature into a single and coherent view constitutes a complex problem to be solved, but which is, however, necessary in order to trigger and control actions as a function of the result of the merge.
A great majority of the studies relating to information merging is devoted to merging homogeneous and essentially low-level numerical data. Other procedures consist in merging low-level data so as to deduce higher-level information therefrom. For example, the data arising from seismic, acoustic, chemical sensors, etc. are merged and interpreted so as to detect, more generally, the presence of a person in a room or the use of a computer. These procedures are, however, concerned with input data which are digital and low-level, even if the output from the merging system is an item of information of higher semantic level. The low-level data are, for example, radar tracks, coordinates of objects, speeds, etc. The interpretation of these data is simple and does not require a general knowledge of the sector of interest.
Certain information merging procedures rely on Dempster-Shafer theory, a theory which generalizes probability theory, and thus uses belief functions. Belief functions are known for their ability to faithfully represent information and the truth of this information. Patent application FR 0705528 from the Applicant is an example of its implementation for merging information arising from independent sensors.
Owing to the generalization of computerized systems and the technological advances in sensors responsive to physical events, information sources have multiplied and diversified. Correlatively, the modalities (text, speech, image, RADAR signal, etc.) under which the information is given have, likewise, multiplied. In order to benefit from the multiplicity of sources and to construct a global representation of the world, it therefore becomes necessary to merge the information together, and to do so whatever its modality. The merging of information may be split into several levels. The first relates to the merging of information pertaining to the characteristics of the objects. This level of merging makes it possible to identify and refine, by merging several observations, the estimation of the characteristics of the objects present in the world. The second level pertains to the merging of objects. This involves appreciating the state of the objects present in the world. The third pertains to the discovery of relations between the various objects present in the world.
One of the aims of the present patent application is to incorporate heterogeneous information by merging it at a high level of representation and by taking account of the semantics that it conveys. The expressions “high level of representation or else high semantics” are used to differentiate the objects aimed at by the present patent application from low-level digital data. Thus the objects processed by the method according to the invention take the form of a sentence, expression, syntax, etc. Thus, one of the objectives of the present patent application is to merge symbols rather than numbers and to have a symbolic representation of objects and heuristics. The heuristics will be expressed as a function of the semantics (i.e. meaning) of the information to be merged.
The publication by Laudy et al, entitled “High-level fusion based on conceptual graphs, in 10th International Conference on Information Fusion, Quebec 2007, and that of 2008, “Information fusion using conceptual graphs: a TV programs case study, in additional Proceeding of the 16th International Conference on Conceptual Structures, Toulouse, France, pp 158-165, propose an approach for symbolic merging relying on the use of conceptual graphs known to a person skilled in the art.
The aforementioned publication of 2008 describes the use of the formalism of conceptual graphs to represent knowledge and information within the framework of a recommendation system for intelligent digital television. The recommendation system analyses the descriptions of the televised programs and decides whether or not to recommend a program to a specific user. Accordingly, the authors use a merging platform to obtain accurate and sure descriptions of televised programs, both as regards programming planning and the description of the content of the program.
The conceptual graphs model proposed by JF Sowa and taken up in the aforementioned publication is essentially composed of a support and of the graphs themselves. A conceptual graph represents several concepts and the relations which exist between them. The conceptual graphs are composed of entity nodes and relation nodes. FIG. 1 represents entities which are drawn in the form of rectangles whereas the relations are ovals. The theory of conceptual graphs relies, inter alia, on the use of a support. The support is a hierarchy of the types of concepts and of relations manipulated. That is to say it involves the set of all the types of objects and relations present in the real world that will be represented, organized in the form of a hierarchy. The support can therefore be viewed as a simplified ontology of the sector of interest which comprises solely the types of objects and the type of relation. A concept node of a conceptual graph is represented by two entities and can be written in the following form: [T: r]. T is the type of concept. It is the type of object of the real world which is represented. r is the value or the measurement observed for the object represented. For example, to represent a temperature of 30 degrees, it will be possible to write the concept [Temperature: 30], where Temperature is the type of the concept and 30 is its value, also called the referent in the subsequent description.
Concerning the merging process itself, it also relies on the conceptual graphs model. The maximal join operation defined by Sowa recalled in the aforementioned articles is used to merge two compatible sub-graphs of two conceptual graphs. FIG. 1 illustrates this operation. Thus, the graph G3 is the result of merging G1 and G2 using the maximal join. However, the use of the maximal join alone is not sufficient to merge information originating from real systems. Real data are indeed noisy and knowledge about the sector is often necessary in order to merge two compatible but different values. For example, observations like a person named “J. Smith” and a person named “M. John Smith” are not equal, but the knowledge parameter prompts the thought that these two observations refer to the same person. This can also apply to data representative of a physical parameter measured by a sensor whose measurement unit is not expressed according to one and the same format. The procedures and devices according to the prior art do not make it possible to address the aforementioned problem area. They are restricted to data stored in numerical form (not character strings, for example) and are implemented within the very simple situational framework, amounting to a measurement or to the state of a characteristic of an object. Here, therefore, one of the objectives is to propose a procedure making it possible to merge information representing complex situations.
Existing approaches to information merging are very broadly geared toward the merging of simple data: merging is carried out so as to obtain the value of a single characteristic of a single object. In contradistinction to these approaches, the method and the system according to the invention make it possible to represent and directly merge information of high semantic level within the framework of complex situations, where several players or objects are involved, linked by spatial, temporal or semantic relations.